Inference Logoinference shell

Mirage Video 1

Generate an expressive talking-head video from a portrait image and an audio track using Mirage Video 1, with natural lip sync, eye contact and micro-expressions.

lip synctalking avatar
run in browser →run via APIrun with your agent
maker
mirage
category
video
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-07-29
price
$0.175/sec

api reference

about

generate an expressive talking-head video from a portrait image and an audio track using mirage video 1, with natural lip sync, eye contact and micro-expressions.

get an api key

create one in settings → api keys. the examples read it from INFERENCE_API_KEY.

bash
1export INFERENCE_API_KEY="inf_your_key"

1. calling the api

install the client

nothing to install. any http client works; the examples use curl, and jq to read responses. every response wraps its result in a data field.

run and get result

send "wait": true to hold the connection until the task finishes. without it the call returns right away with the task still running.

bash
12# "wait": true holds the connection until the task finishes.3# the response is {"data": {"id", "status", "status_text", "output", ...}}4curl -X POST https://api.inference.sh/apps/run \5  -H "Authorization: Bearer $INFERENCE_API_KEY" \6  -H "Content-Type: application/json" \7  -d '{8    "app": "mirage/video-1",9    "input": {10      "image": "https://example.com/file.png",11      "audio": "https://example.com/file.png"12    },13    "wait": true14  }'

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks. send "stream": true and the response is a server-sent event stream that ends when the task does.

bash
12# "stream": true answers with server-sent events until the task finishes3curl -N -X POST https://api.inference.sh/apps/run \4  -H "Authorization: Bearer $INFERENCE_API_KEY" \5  -H "Content-Type: application/json" \6  -d '{7    "app": "mirage/video-1",8    "input": {9      "image": "https://example.com/file.png",10      "audio": "https://example.com/file.png"11    },12    "stream": true13  }'

run in the background

start the task and get its id back right away, then check on it later. for long tasks, or when you'd rather be called back, see webhooks.

bash
1# without "wait" the call returns the task right away (status 1, "received")2TASK_ID=$(curl -s -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "mirage/video-1",7    "input": {8      "image": "https://example.com/file.png",9      "audio": "https://example.com/file.png"10    }11  }' | jq -r '.data.id')1213# later: read its state. done when status is 10 (completed), 11 (failed) or 12 (cancelled)14curl -s https://api.inference.sh/tasks/$TASK_ID \15  -H "Authorization: Bearer $INFERENCE_API_KEY" | jq '.data | {status, status_text, output}'

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

pass urls directly or use data uris for inline content.

bash
1# pass URLs or data URIs as input values2curl -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "mirage/video-1",7    "input": {8      "image": "https://example.com/image.png",9      "audio": "data:audio/mp3;base64,..."10    },11    "wait": true12  }'

manual upload

you can also upload files manually and use the returned url.

bash
1# 1. create the file: the response has an upload_url and the file's uri2FILE=$(curl -s -X POST https://api.inference.sh/files \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{"files": [{"filename": "image.png", "content_type": "image/png"}]}')67# 2. PUT the bytes to upload_url8curl -X PUT "$(echo "$FILE" | jq -r '.data[0].upload_url')" \9  -H "Content-Type: image/png" \10  --data-binary @image.png1112# 3. pass the uri as an input value13echo "$FILE" | jq -r '.data[0].uri'

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

bash
1curl -X POST https://api.inference.sh/apps/run \2  -H "Authorization: Bearer $INFERENCE_API_KEY" \3  -H "Content-Type: application/json" \4  -d '{5    "app": "mirage/video-1",6    "input": {7        "image": "https://example.com/file.png",8        "audio": "https://example.com/file.png"9    },10    "webhook": "https://your-server.com/webhook"11  }'

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

imagestring(file)*

portrait image to animate (jpeg or png). use a clear, front-facing shot of a single person in a close to medium framing, with the mouth open. closed mouths and multiple faces in frame both degrade the result.

audiostring(file)*

speech audio that drives the performance (wav or mp3). the output video runs as long as this clip. expressive, natural-sounding audio gives the best result — audibly synthetic voices look worse on screen.

output

videostring(file)*

the generated talking-head video.

video_idstring*

mirage video id for this generation, useful when following up on a specific run with support.

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  5. Rodin Gen-2.5

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